Context Gating

Context gating is a technique used in artificial neural networks to improve efficiency and mitigate catastrophic forgetting in continual learning scenarios. Current research focuses on developing algorithms and architectures, such as Mixture-of-Experts models and spiking neural networks with context-dependent gating, that dynamically select relevant information for each task, improving performance and reducing computational costs. This approach is inspired by biological mechanisms in the brain and shows promise for enhancing the capabilities of AI systems in various applications, including medical image analysis and large language models deployed on resource-constrained devices. The ultimate goal is to create more robust and efficient AI systems that can learn and adapt continuously, mimicking the human brain's ability to handle multiple tasks without interference.